United States v. Obinna Ukwu , 546 F. App'x 305 ( 2013 )

UNPUBLISHED
UNITED STATES COURT OF APPEALS
FOR THE FOURTH CIRCUIT
No. 12-4866
UNITED STATES OF AMERICA,
Plaintiff - Appellee,
v.
OBINNA FELIX UKWU,
Defendant - Appellant.
Appeal from the United States District Court for the District of
Maryland, at Baltimore. Catherine C. Blake, District Judge.
(1:12-cr-00134-CCB-1)
Submitted: August 21, 2013 Decided: November 22, 2013
Before NIEMEYER, GREGORY, and DUNCAN, Circuit Judges.
Affirmed by unpublished per curiam opinion.
Bruce Fein, BRUCE FEIN & ASSOCIATES, INC., Washington, D.C.,
for Appellant. Rod J. Rosenstein, United States Attorney,
Kathleen O. Gavin, Assistant United States Attorney, OFFICE OF
THE UNITED STATES ATTORNEY, Baltimore, Maryland, for Appellee.
Unpublished opinions are not binding precedent in this circuit.
PER CURIAM:
Appellant Obinna Ukwu was convicted of twelve counts of
aiding and assisting in the preparation of false income tax
returns. 26 U.S.C. § 7206(2). Mr. Ukwu was sentenced to 51
months in prison. He now challenges this sentence, arguing that
the district court erred when it estimated the amount of tax
loss Mr. Ukwu caused. Because a preponderance of the evidence
supports the district court’s estimate, we affirm the sentence.
I.
Mr. Ukwu was an officer with the Maryland State Division of
Corrections, but in 2006, he started an accounting business as
side employment. The business offered tax return preparation
services, and Mr. Ukwu operated the business until midway
through 2010, when his legal problems began. In the intervening
years, business boomed: in 2006, his revenue was roughly
$8,000, but by 2009, it soared to $175,000.
A criminal investigation in 2010 revealed that Mr. Ukwu’s
business was less criminally successful than successfully
criminal. On many of his clients’ returns, Mr. Ukwu would claim
fictional business losses in order to garner tax benefits. At
trial, the vast majority of witnesses testified that these
losses were entirely false and that they were not aware that
Mr. Ukwu had invented these numbers on their returns.
2
Mr. Ukwu’s malfeasance went beyond false business losses.
Mr. Ukwu claimed false charitable deductions on his clients’
forms. He also committed tax fraud on his own income taxes,
filing a joint return for his wife and himself, but also filing
a separate individual return for his wife under a different
name. Finally, Mr. Ukwu took fees from his clients’ bank
accounts and refund checks without notification.
After Mr. Ukwu’s jury conviction, the government estimated
how much money Mr. Ukwu took from federal and state coffers. It
concluded that Mr. Ukwu’s criminal behavior created tax losses
of $2.1 million, which corresponds to a base offense level of 22
under § 2T4.1 of the United States Sentencing Guidelines Manual.
On appeal, Mr. Ukwu takes issue with the $2.1 million
estimate, arguing that a preponderance of the evidence shows
that his ill-gotten gains amounted to less than $1 million.
Specifically, he argues that the district court’s method of
estimating the tax shortfall was unsound because it used a
small, flawed sample of tax returns to make inferences about
another 1000 returns that he prepared. Based in part on its
estimate, the district court sentenced Mr. Ukwu to 51 months in
prison. Mr. Ukwu filed a timely appeal.
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II.
We have jurisdiction to review Mr. Ukwu’s sentence under 28
U.S.C. § 1291 and 18 U.S.C. § 3742. The government has the
burden of establishing the amount of tax loss by a preponderance
of the evidence. United States v. Mehta, 594 F.3d 277, 281 (4th
Cir. 2010). The district court need not calculate the amount
with a pharmacist’s precision: the sentencing guidelines
require only a reasonable estimate. Id. Further, the district
court may consider any relevant information regardless of its
admissibility, provided that the information is sufficiently
reliable. Id.While we generally review for clear error, Mr. Ukwu did not
challenge the district court’s tax loss estimate at sentencing.
Therefore, we will apply a plain error standard of review.
United States v. Slade, 631 F.3d 185, 188 (4th Cir. 2011).
Mr. Ukwu must demonstrate that an error was made, that the error
was plain, and that the error affected his substantial rights.
Id. at 190. In the sentencing context, an error affects
substantial rights if a different sentence would have been
imposed absent the error. Id. In addition, even if these three
elements are met, we retain discretion over whether to correct
the forfeited error and do not exercise this discretion “unless
the error seriously affects the fairness, integrity or public
reputation of judicial proceedings.” United States v. Olano,
4
507 U.S. 725, 732 (1993) (internal quotations and citations
omitted).
Mr. Ukwu takes issue with how the district court reached
its conclusion that his crimes caused over $1 million in tax
losses. The sentencing court faced a difficult problem because
of the sheer size of Mr. Ukwu’s potential fraud. Mr. Ukwu
prepared roughly 1,000 tax returns that reported business
losses, but the sentencing court and the IRS do not have time to
audit each return, interview each taxpayer, and identify the
extent of Mr. Ukwu’s crimes. As a result, the government had to
rely on sampling techniques to make inferences about the
universe of 1,000 tax returns. Essentially, the government had
to take a spoonful of sauce out of the pot to assess whether the
whole batch was spoiled.
The government used two samples of Mr. Ukwu’s 1,000
prepared tax returns to answer the following question: how
often did Mr. Ukwu invent Schedule C losses from whole cloth?
First, the government relied on a sample of 18 returns that were
used at Mr. Ukwu’s criminal trial. These returns all reported
Schedule C losses and contained loss descriptions that were
vague, undocumented, and suspicious. Based on the testimony
from the taxpayers involved, the government concluded that 16
out of 18 returns had Schedule C losses that were entirely
false. The two remaining returns were disputed. Using these
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numbers, the government found that 88.88% of the returns in this
sample used entirely false Schedule C losses. Note, however,
that the returns investigated at trial were chosen for
investigation specifically because they contained very high tax
loss amounts. Thus, this was not a random sample of returns.
To solve this problem, the government then collected a
random sample of returns to confirm its initial findings. The
government drew 24 returns from the universe of 1,000 returns
that contained Schedule C losses. 1 Then, investigators analyzed
these returns and found that every single one had large Schedule
C losses that were vague, undocumented, and suspicious. That
is, these returns exhibited the same pattern questionable
Schedule C descriptions as the non-random sample of returns that
were investigated at trial.
1
Specifically, the investigators alphabetized the returns
by the first name of the taxpayer, then drew one out of every
fifty returns. This technique passes muster, though it is not
perfect. Mr. Ukwu is Nigerian, and many of his clients were
Nigerian immigrants. If these immigrants were more likely to
have the same first name, or the same first letter of their
first name, and if Mr. Ukwu was more likely to file false
returns on immigrants’ forms, as the district court suggested,
then the sampling technique would be problematic. However,
given the burden of proof—simply a preponderance of the
evidence—it is more likely than not that this issue was not so
grave that it affected the outcome of the sentencing
calculation. Thus, while this technique does not warrant
reversal here, future sentencing courts should be wary of
accepting at face value that a randomization technique is truly
random.
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In sum, the government analyzed a non-random sample of
returns at trial and found that 90% of the Schedule C losses
were entirely false. Then, investigators used a random sample
to confirm this estimate, reasoning that since the random sample
bore the same patterns as the non-random sample, the two samples
likely contained similar levels of fraud. That is, since the
random sample looked like the non-random one, and since 90% of
returns in the non-random sample were completely false, then 90%
of the random sample was also likely to be completely false.
Finally, the government used this 90% number to calculate
Mr. Ukwu’s tax loss estimate. The investigators could establish
that among the 1000 returns where a Schedule C loss was claimed,
Mr. Ukwu claimed roughly $16.4 million in Schedule C losses. If
90% of these losses were entirely fabricated, then this means
that roughly $14.6 million of false losses were claimed.
Assuming the lowest marginal tax rate of 10%, and factoring in
state tax losses, the estimated tax loss was roughly $2.1
million. Because this estimate is between $1 million and $2.5
million, the district court concluded that Mr. Ukwu merited a
base offense level of 22. U.S.S.G. § 2T4.1.
Mr. Ukwu takes issue with several methodological moves made
by the government in reaching its $2.1 million estimate. First,
he argues that the samples used were too small. Second, he
argues that it was error to rely on the non-random sample of
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returns. Third, he argues that the government never established
that the $14.6 million in Schedule C losses were totally
fraudulent, rather than partially fraudulent.
A.
As a preliminary matter, we can reject with ease Mr. Ukwu’s
argument that the government’s samples were too small to make a
robust inference about the universe as a whole. His argument
has intuitive appeal—how can 24 cases tell us about 1000? But
Mr. Ukwu’s claim that small sample sizes render estimates
useless is statistically incorrect. See David H. Kaye & David
A. Freedman, Reference Guide on Statistics, in Reference Manual
on Scientific Evidence 83, 126 n.145 (2d ed. 2000) (“Analyzing
data from small samples may require more stringent assumptions,
but there is no fundamental difference in” how we make
statistical inferences in small versus large samples).
Certainly, a larger sample size is preferable, since it
decreases the odds that one’s sample will be misleading. 2 See
2
Specifically, statisticians teach that larger sample sizes
can cut down on two types of error. First, there is the
possibility that Mr. Ukwu committed rampant corruption, but by
chance, we end up with a sample of cases where he did nothing
wrong. Sanders, Bendectin, supra, at 342–43. Second, there is
the possibility that Mr. Ukwu committed almost no corruption,
but we happen to end up with a sample of cases in which he
appears to fudge numbers constantly. Id. A larger sample size
decreases the chance of both false negatives and false
positives. Id.
8
Joseph Sanders, The Bendectin Litigation: A Case Study in the
Life Cycle of Mass Torts, 43 Hastings L.J. 301, 342–43 (1992).
However, even very small samples can be useful, as any political
polling agency can attest: in many elections, a sample of 1,000
Americans can show, with enough certainty to satisfy the
preponderance of the evidence standard, what is likely to happen
in an election involving over 100 million voters. See Nate
Silver, The Signal and the Noise 63 fig.2-4 (2012). While 24 is
a relatively small sample, it amounts to 2% of the entire
universe. This sample size does not paralyze us in our attempts
to make inferences about the universe of all cases. See United
States v. Littrice, 666 F.3d 1053, 1061 (“[R]equiring the
government to go through all the needles in the haystack of
materially fraudulent and false returns . . . would place a
burden on the government beyond what the preponderance standard
requires.”). As any chef or statistician can attest, even a
small spoonful of sauce can indicate how much salt to add.
Mr. Ukwu’s next argument is that the government’s estimate
was erroneous because it relied on a non-random sample, but this
argument is similarly unavailing. He cites to Mehta, in which
we questioned a district court’s use of a non-random sample to
estimate the amount of tax loss among a broader universe of
returns. 594 F.3d 277 (4th Cir. 2010). In Mehta, the
government analyzed a sample of returns that were chosen because
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they had been audited by the IRS. Id. at 282–83. It calculated
the average tax loss among these returns to be $1,531 and then
concluded that the entire universe of returns would have a
similar average tax loss. Id. This was problematic because the
returns in the sample were flagged by the IRS specifically
because they were more likely to contain tax losses. Id. As
such, the average amount of tax loss among this sample was
misleading: the broader universe of returns was likely to have
a lower average tax loss. Id. The sentencing court’s tax
estimate was like using a group of NBA players to estimate the
average height of all Americans.
Mr. Ukwu is correct that the initial, non-random sample
used in this case is a problematic tool to make inferences about
the amount of tax loss for the broader universe of returns. The
returns chosen for the non-random sample were chosen
specifically because they had higher tax losses. It could be
that the amount of fraud in these returns was higher than for
the entire universe of returns, so relying on the non-random
sample alone would be problematic. However, the government’s
tax loss estimate was based on more than a non-random sample.
The government went out of its way to collect a random sample of
returns to bolster its initial estimate. It compared this
random sample to the original, non-random sample, and the
government concluded that both groups of returns contained the
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same pattern of suspicious, unexplained tax losses. Though the
government’s original estimate is based on a non-random sample,
the government cleansed this error with the use of a random
sample. Thus, the district court did not make the sort of
mistake identified in Mehta, and as such, it did not commit
plain error. See Olano, 507 U.S. at 734 (1993) (“‘Plain’ is
synonymous with ‘clear’ or, equivalently, ‘obvious.’”).
Mr. Ukwu’s final argument is most challenging. He admits
that the non-random sample contains 90% falsehoods. He admits
that the random sample looks similar to the non-random sample.
However, he argues that this similarity alone fails to prove
that in the random sample, all of the unexplained Schedule C
losses were due to criminality. Instead, these losses might
have been exaggerated instead of false, or due to negligence
instead of fraud. Mr. Ukwu points to a Seventh Circuit case in
which that court expressed skepticism of a similar methodology.
United States v. Schroeder, 536 F.3d 746, 754–55 (7th Cir.
2008).
Mr. Ukwu’s argument fails because the government need only
make a reasonable estimate of the tax loss, and the methodology
here, though imperfect, meets that standard. U.S.S.G. § 2T1.1
cmt. 1; Mehta, 594 F.3d at 282. In the eighteen tax returns
investigated at trial, the Schedule C forms Mr. Ukwu prepared
exhibited a suspicious pattern. Many returns claimed that the
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taxpayer worked as a contractor for Mary Kay or worked in
“Nursing Services,” but at trial, the taxpayers testified that
they never worked for Mary Kay and never owned such health care
businesses. These returns also contained a suspicious pattern
of receipts and expenses. The invented businesses often had
revenues that were low or non-existent. Nearly all expenses
were low or non-existent. Labor costs, meanwhile, were
enormous.
The government’s random sample of tax returns exhibited a
similar or identical pattern. Many of the returns listed Mary
Kay as a profession; many more listed nursing services. One
return even listed “General Services” as the profession. In the
random sample, as in the non-random sample, the businesses
almost always claimed to have zero sales, zero expenses, but
enormous labor costs. Given these similarities, the sentencing
court made no plain error when it concluded that, just like the
returns analyzed at trial, the random sample of returns
contained business losses that were entirely fabricated. See
Olano, 507 U.S. at 734 (“‘Plain’ is synonymous with ‘clear’ or,
equivalently, ‘obvious.’”).
Further, Mr. Ukwu’s reliance on Schroeder is misguided. In
that case, the government used a similar argument to make a tax
estimate: it found strong evidence of fraud in sample A, found
a similar pattern of losses in sample B, and concluded that
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sample B was therefore likely to contain fraud. 536 F.3d at754–55. The Seventh Circuit expressed skepticism of this
methodology. Id. at 755. However, the court’s reversal in that
case was based not on the sampling methodology but rather on
fundamental legal errors made by the sentencing court. Id. at
755. The district court in that case applied the wrong burden
of proof, apparently concluding “that if evidence is admissible
it proves the truth of the proposition for which it is being
offered.” Id. Instead of requiring the government to prove a
tax loss by a preponderance of the evidence, the sentencing
court accepted the government’s estimate without any analysis,
concluding that as long as the evidence was reliable, the tax
loss had been proven. Id. Here, meanwhile, the sentencing
court conducted a careful analysis of the evidence. It noted
potential shortcomings in the methodology but concluded that the
estimate was more likely than not to be accurate or
significantly lower than the true tax loss. Thus, Schroeder is
inapposite. Though the government’s methods were not perfect,
its tax loss estimate was reasonable. Further, unlike in
Schroeder, the district court’s analysis was careful and legally
sound. This is all that is required under the Sentencing
Guidelines. U.S.S.G. § 2T1.1 cmt. 1; Mehta, 594 F.3d at 282.
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B.
Finally, even if Mr. Ukwu is correct that the tax loss
estimate has methodological shortcomings, these errors were
harmless and therefore did not affect his substantial rights.
Slade, 631 F.3d at 190. The government estimated a tax loss of
$2.1 million. Mr. Ukwu argues that it is possible that most of
the claimed Schedule C losses were not criminal, but instead
were legitimate losses, or at least negligent ones. For
example, a client might have had $1,000 in legitimate business
losses, but Mr. Ukwu might have pumped the number up to $2,000.
Mr. Ukwu might be correct, but the $2.1 million estimate is
so conservative that even if he is right, the total tax losses
are still likely to be above $1 million, which is the level of
loss that is necessary for his sentencing range. U.S.S.G.
§ 2T4.1. First, in addition to false Schedule C losses,
Mr. Ukwu used false charitable deductions on his clients’
returns, and none of these deductions were counted towards the
$2.1 million figure. In one case, Mr. Ukwu claimed a $10,000
charitable gift that was entirely fabricated, suggesting that
his Schedule A fraud might be significant. Similarly, the $2.1
million figure also excludes the fraud Mr. Ukwu committed on his
own tax returns, which amount to roughly $100,000.
Further, the court’s estimate only looked at Mr. Ukwu’s
returns from 2006 to 2008. He continued to prepare tax returns
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in 2009 and for part of 2010, and none of these returns were
factored in to the tax loss estimate. Factoring in Mr. Ukwu’s
2009 returns increases the estimated loss to roughly $3 million.
Most importantly, the $2.1 million figure was calculated by
applying a 10% marginal tax rate to the entire universe of
returns. This is likely a gross underestimate of the true tax
liability, since many of the returns were likely to have been
subject to a 25% marginal tax rate or higher. This alone could
increase the estimated tax loss by more than two-fold. In sum,
even if Mr. Ukwu’s arguments are valid, his estimated tax losses
are more likely than not to be well over $1 million. As such,
the district court’s alleged error did not affect his
substantial rights.
For the foregoing reasons, we affirm the judgment of the
district court. We dispense with oral argument because the
facts and legal contentions are adequately presented in the
materials before this court and argument would not aid the
decisional process.
AFFIRMED
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